rm mean op

python/akg/ms/gpu/__init__.py

@@ -26,7 +26,6 @@ from .logical_or import LogicalOr
 from .relu6_grad import ReLU6Grad
 from .squeeze import Squeeze
 from .squeeze_grad import SqueezeGrad, gpu_schedule_SqueezeGrad
-from .mean import SimpleMean
 from .sub import Sub
 from .mul import Mul
 from .hsigmoid import HSigmoid

python/akg/ms/gpu/mean.py

-#!/usr/bin/env python3
-# coding: utf-8
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""mean op compute and schedule"""
-from .default_schedule import DEFAULT_GPU_THREAD
-from akg.ops.math_gpu.sum_value import sum_value
-import akg
-from akg.ops.math_gpu.mean import mean
-
-def gpu_schedule_Mean(outs):
-    """
-    gpu schedule function for mean.
-
-    Args:
-        outs (tvm.tensor.Tensor): outputs of compute.
-
-    Returns:
-        sch (schedule.Schedule): The created schedule.
-    """
-    out = outs[0] if isinstance(outs, list) else outs
-
-    sch = tvm.create_schedule(out.op)
-    if out.op.name == "T_divide":
-        tensor_c = out
-    else:  # squeeze
-        tensor_c = out.op.input_tensors[0]
-
-    tensor_b = tensor_c.op.input_tensors[0]
-    if len(tensor_c.op.axis) >= 2:
-        sch[tensor_b].compute_at(sch[tensor_c], tensor_c.op.axis[1])
-    else:
-        sch[tensor_b].compute_at(sch[tensor_c], tensor_c.op.axis[0])
-
-    bx, tx = sch[tensor_c].split(tensor_c.op.axis[0], factor=DEFAULT_GPU_THREAD)
-    sch[tensor_c].bind(bx, tvm.thread_axis("blockIdx.x"))
-    sch[tensor_c].bind(tx, tvm.thread_axis("threadIdx.x"))
-    return sch
-
-@akg.schedule(gpu_schedule_Mean)
-def Mean(data, axis=None, keepdims=False):
-    return mean(data, axis, keepdims)
-
-@akg.schedule(gpu_schedule_Mean)
-def SimpleMean(x):
-    """
-    SimpleMean compute the mean of the input 4D Tensor over last two axises and keep reduced dimensions.
-
-    Args:
-        x (tvm.tensor.Tensor): Tensor of type float16, float32.
-
-    Returns:
-        tvm.tensor.Tensor, has the same type as x, output shape will be (a, b, 1, 1) if input Tensor x is (a, b, c, d).
-    """
-    axis = (2, 3)
-    keepdims = True
-    return mean(x, axis, keepdims)

python/akg/ops/math_gpu/mean.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""operator dsl function: mean"""
-import akg.topi
-import akg.tvm
-from akg.utils import format_transform as ft_util
-from akg.utils import validation_check as vc_util
-from akg.ops.math_gpu import sum_value
-
-
-@vc_util.check_input_type(akg.tvm.tensor.Tensor, (list, tuple, int, type(None)), (bool, type(None)))
-def mean(data, axis=None, keepdims=False):
-    """
-    Computes the mean of the values of a Tensor over the whole dataset.
-
-    Args:
-        data (tvm.tensor.Tensor): Tensor.
-        axis (Union[list, tuple, int, None]): If the tuple is empty, the axis equal to None.
-        keepdims (bool): If keepdims equal to True, the result shape length is same to input shape length.
-
-    Returns:
-            tvm.tensor.Tensor, has the same type as data. If keepdims equal to True, all reduced dimensions are
-            retained with length 1. else these reduced axis will be eliminate.
-    """
-    shape = [x.value for x in data.shape]
-    vc_util.reduce_axis_check(shape, axis)
-    axis = ft_util.refine_reduce_axis(data, axis)
-
-    count = 1
-    for i in axis:
-        count *= shape[i]
-    output, _ = sum_value.sum_value(data, axis, keepdims)
-    res = akg.topi.divide(output, count)
-
-    return res